The main principle of license plate recognition is to recognize the characters in the license plate which indicates the identity of the vehicle. This research will provide a system which can be implemented to the automatic payment in highway. Indonesian license plate consists of two parts, every of which has certain characters. These characters may become problem in the recognition process. Another problem is on the type of the license plate since Indonesia applies different color for every type of vehicle. In this research, different approaches are employed in the recognition of license plate; that is using character area as the feature value, also known as feature area, and K-Nearest Neighbor (KNN) as classification method. In addition, another method that has been used in our previous research is also employed to detect the character of license plate. The result shows very significant accuracy of 99.44%. In the process of recognition, scenario 1 gives the best accuracy at the K-1 value; that is 68.57% on the license plate and 92.72% on the characters of license plate. In the scenario 2 was obtained the license plate accuracy of 52% and license plate character accuracy of 89.36% with K-5. The system ran in a relatively short computational time.

1. TELKOMNIKA, Vol.17, No.2, April 2019, pp.620~627
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v17i2.9017 ◼ 620
Received February 22, 2018; Revised November 27, 2018; Accepted December 29, 2018
Indonesian license plate recognition based on area
feature extraction
Fitri Damayanti1
, Sri Herawati2
, Imamah3
, Fifin Ayu M4
, Aeri Rachmad*5
Faculty of Engineering, University of Trunojoyo Madura, Indonesia
Raya Telang Street PO BOX 02 Kamal-Bangkalan, 69192, Indonesia
*Coresponding author, e-mail: fitri2708@yahoo.com1
, aery_r@yahoo.com5
Abstract
The main principle of license plate recognition is to recognize the characters in the license plate
which indicates the identity of the vehicle. This research will provide a system which can be implemented
to the automatic payment in highway. Indonesian license plate consists of two parts, every of which has
certain characters. These characters may become problem in the recognition process. Another problem is
on the type of the license plate since Indonesia applies different color for every type of vehicle. In this
research, different approaches are employed in the recognition of license plate; that is using character
area as the feature value, also known as feature area, and K-Nearest Neighbor (KNN) as classification
method. In addition, another method that has been used in our previous research is also employed to
detect the character of license plate. The result shows very significant accuracy of 99.44%. In the process
of recognition, scenario 1 gives the best accuracy at the K-1 value; that is 68.57% on the license plate and
92.72% on the characters of license plate. In the scenario 2 was obtained the license plate accuracy of
52% and license plate character accuracy of 89.36% with K-5. The system ran in a relatively short
computational time.
Keywords: area feature extraction, character recognition of license plate, K-Nearest Neighbor,
license plate recognition
Copyright © 2019 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
License plate is an identity for a vehicle which indicates that the vehicle has been
registered. With this identity the ownership of the vehicle can be identified. The system of
license plate in Indonesia is based on the residency area in which the vehicle is registered and
on the registration order. Some countries have many versions on the form of license plate, for
example Tunis [1], China [2], Libya [3], and India [4]. These countries have one-line license
plate. It is different from Indonesia which applies two-line license plate. The first line contains
one or two letters denoting the area registration, one to four digit numbers, and one or two
letters denoting the region the vehicle registered. The second line consists of four digits
indicating information about month and year when the plate will expire. Besides the form,
Indonesia also applies color to the system of license plate, i.e. private vehicle (black),
government vehicle (red), public vehicle (yellow), and dealer vehicle (white).
This research is aimed to create a license plate recognition system. This system later
can be used as the reference to identify vehicles. The challenge in the process of license plate
identification is how to detect and recognize the license plate of vehicles in different form and
type like the one in Indonesia. Some previous research in recognition of Indonesian license
plate have been carried out [5–7]. A research [5] employing Fourier transform method and
hidden markov model obtained an accuracy of 84.38%. Another research [6] resulted accuracy
value of 85% on the character recognition and of 97% on the process of detecting the location
of license plate. This research uses contour in detecting the location. The process continues by
doing figure segmentation utilizing interconnected figure segmentation, and is ended in the
process of classification by using static classification technique by which the segmented figure
is translated into the character of ASCII. However, a problem aroused when a character cannot
be recognized because of its different shape or italic shape.
A high degree of accuracy, that is 96%, is gained in the other previous research [7].
This research implemented a method of discrete cosine transform to extract feature and radial
basis function as the classification method. Yet, this research was conducted only on the
recognition of one type of license plate, i.e. black license plate (private vehicle).

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Indonesian license plate recognition based on area feature extraction (Fitri Damayanti)
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Considering the above problem, we develop a system to solve the problem related to
the system of Indonesia’s license plate. The system developed is expected to be able to
recognize all types of license plate of vehicles in Indonesia. Moreover, this research will create a
system which is able to run in a short computational time with a high degree of accuracy. To
gain the two objectives of the research, area feature is chosen as feature extraction method;
while K-nearest neighbor, which has been used in some previous research [8–12], is
implemented to recognize license plate.
2. Research Method
In order to develop a system of license plate recognition, the first step is identifying the
characters on the plate. They are in the forms of letters A to Z and digits 0 to 9. Figure 1 [13]
presents the forms and types of license plate used in this research.
.
(a) (b) (c) (d)
Figure 1. Vehicle license plate in Indonesia (a) private vehicle, (b) government vehicle,
(c) public vehicle, dan (d) dealer vehicle [13]
After identifying the characters, the next steps are resizing the image, converting the
image, detecting the characters of the vehicle’s license plate, and the last is recognizing the
characters of the plate as shown in Figure 2. The process begins by resizing the images of the
plates. The real image is resized into 300x720 pixels in order to ease the next processes.
Conversion process is done by converting the captured image which is in the form of RGB into
grayscale image, as the following (1)
)1140.0()5870.0()2989.0( BGRG ++= (1)
The process continues detecting the plate by identifying the characters on the plate. By
using area feature extraction the characters identified are processed to get the feature value.
Lastly, the recognition process is done by utilizing K-NN method to recognize the plate based on
the feature value obtained.
Figure 2. System design
3. Results and Discussion
3.1. Detection of License Plate
Detecting license plate is done to separate the object (the character) from its
background and capture the characters which are the vehicle’s license number on the first line
representing the area registration, license number, and region. This process aims to get the
characters on the plate, which can be done using some techniques [14–17]. Based on the types
of the plate which have different color of background and characters, a new approach has been
Image of Number Plate Resize the Image
Convert Image
Detection of Number
Plate
Recognition of Number
Plate

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developed [14] by utilizing area and ratio image. The approach is developed after conducting an
analysis on the characters of the plate. The process of detecting the character of the license
plate is explained in Figure 3.
The process of detecting characters begins by processing the grayscale image obtained
from the previous process. Then, the process continues converting the grayscale image into two
kinds, i.e. binary image and complement drawing. It is done in order to get a white pixel value of
1 on the two images since the plates have different types as shown in Figure 1. The next is
adding up the pixel value of 1 on the two images. To get the character on the plate, selection
process on the two images is carried out, thus there is only one image selected. This process is
done by taking the lowest total pixel value between the two images. The process of selection is
illustrated in Figure 4 [14].
Figure 3. The process of detecting the character of the license plate [13]
Figure 4. Selection of license plate, (a) original images, (b) grayscale images,
(c) binary images, (d) complement drawings, (e) selected image [13]

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Indonesian license plate recognition based on area feature extraction (Fitri Damayanti)
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Then, method of connected component is applied as an algorithm to detect
interconnected region blob on a binary image. A previous research by Arai and Tolle [18] uses
connected component as a method to detect text in comics. The next process is can it is obtain
dilate selection which is the character of the plate by using area of blob. To determine the blob
area threshold value of 1500 is used. This process will select when a blob area is higher than or
equal to 1500 pixels, then the area is detected as the character, as (2) below:
𝑏𝑙𝑜𝑏𝑖 = {
𝐶ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟 𝑎𝑟𝑒𝑎𝑖 ≥ 1500
𝑁𝑜𝑡𝐶ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟 𝑎𝑟𝑒𝑎𝑖 < 1500
} (2)
From the candidate selection process by using area of blob region, the character of the
plate is obtained. Nevertheless, a problem aroused, as illustrated in Figure 1 (a), where there is
a horizontal line detected as the character for its blob area value >1500. Regarding to the
problem, a calculation on image ratio is carried out. The image ration is gained from the
difference of scalar value of major axis length and scalar value of minor axis. From that ratio
value, to determine the candidate character a condition is given on the ratio value of the image,
like the (3). This process is to eliminate another object whose shape is not like the object or the
character of license plate which is in a rectangle.
𝑟𝑎𝑡𝑖𝑜 = {
𝐶ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟 𝑟𝑎𝑡𝑖𝑜 > 30&𝑟𝑎𝑡𝑖𝑜 < 300
𝑁𝑜𝑡𝐶ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
} (3)
Detected object as the character will be analyzed to find out the area value surrounding
the rectangle object, called as bounding box. Then, cutting process is done based on the area
of bounding box; that is the area surrounded by four coordinate points ((𝑥 𝑚𝑖𝑛,𝑦 𝑚𝑖𝑛 ),
(𝑥 𝑚𝑖𝑛,𝑦 𝑚𝑎𝑥 ), (𝑥 𝑚𝑎𝑥,𝑦 𝑚𝑖𝑛 ), (𝑥 𝑚𝑎𝑥,𝑦 𝑚𝑎𝑥 )). Figure 5 shows the detecting process done started from
grayscale process to segmenting license plate's character.
Figure 5. Result of detection process of license plate (a) grayscaleimage,
(b) image of selection, (c) detection of character, (d) character of license plate [13]
3.2. Recognition of License Plate
The last process is recognition. The character obtained in the process of detecting the
license plate is used as the input. The steps can be seen in Figure 6. Firstly, the process is
done by resizing the character into 200x100 pixels, in that the size of every character is same in
order to make the process of recognition easy. After that, feature extraction process is carried
out to get feature value of the character. By considering computational time, the area of
character figure is utilized as the reference in determining feature extraction method.
The first step is that from the real image the character image is divided into 10x10 areas
(10 rowsx10 columns) as shown in Figure 7, and thus in each area is obtained 20x10 pixel.
Then, because the image processed is binary image, addition process is done by adding the
value of 1 of each area. The process of saving feature is by placing the value of every area
orderly started from area 𝑋1, 𝑋2, 𝑋3, 𝑋4, … … . 𝑋100. Hence, feature value of 100 is gained in every
character as shown in Figure 8 [19].

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Figure 6. Recognition process Figure 7. Features with 10x10 areas. (a) the
character of the letter B, (b) the character of license
(a) (b)
Figure 8. Feature saving (a) illustration of area-based feature caption,
(b) area-based feature value placement in saving process
The classification process was done from the feature gained in the previous process. In
the recognition process the method of K-Nearest Neighbor (KNN) was employed. KNN [20] is a
learning method that is good and easily used to solve problem of recognition. Similar to the
existing learning method [21-23], KNN works by comparing the feature obtained, (in this
research it is feature resulted from license plate character extraction) with (training data) to get
the shortest distance. Before the classification process, we did a learning process by training 10
images in every alphabet and in every number, thereby the overall training data was as many as
360 data. Based on the training data, in the process of K-NN we searched the shortest distance
between the 360 training data and the trial data. The point K has a function as the class
determinant based on the average of the shortest classes. The measurement of the shortest
distance used Euclidean distance with (4) [24].
2
1
)(),( 
=
=
−=
ni
i
ii yxyxd (4)
Start
Character image of
the number plate
Resize image
Area feature
extraction
Classification
with K-NN
Number plate
End

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3.3. Analysis Trials
The trials was done by testing 50 data taken randomly. The first trial was detecting the
character on the license plate by using a method that has been used previously [13]. As soon as
the result was obtained, the next trial was done by using 2 scenarios; as follows:
a) Scenario 1, detected character was processed by using are feature extraction, i.e. 10x10
areas. Classification process used K-NN by applying K-values of 1, of 3, and of 5.
b) Scenario 2, detected character was processed by using are feature extraction, i.e. 20x20
areas. Classification process used K-NN by applying K-values of 1, of 3, and of 5.
The result of detecting process on 50 trial data which were taken randomly from all
types of plate as shown in Figure 1 shows accuracy of 99.44%. From all characters on 50 data
trial, error occurred only on detecting 2 characters. The error on the first character was that it
was not matched with the defined ratio in the detection process, so the character was
undetected. This error made the character fail to be recognized in the recognition process. The
second error occurred when a noise was detected as a character. It happened because the
noise, whose condition matched with the defined condition, passed the blob selection as well as
the ratio process. Hence, it can be said that the accuracy obtained by our system was very high.
The result of recognition of the license plate and the plate’s character indicates that two
scenarios give different results. In the scenario 1 as shown in Figure 8, the accuracy of the
license plate did not change; that is 60% accuracy; whereas the accuracy of the character
underwent a reduction when K-value increased. In the scenario 2 as shown in Figure 9, the
accuracy of license plate and of the character increased when K-value increased. From the two
results, we can conclude that the feature extraction process caused the different accuracy result
of the two scenarios. The more we divide the area in the feature extraction process, the higher
accuracy cause is when K-value is added. The best accuracy was gained in scenario 1 despite
the fact that this scenario does not affect the accuracy gain of the license plate, even decreases
the accuracy of the plate’s character.
Figure 8. Recognition results of scenario 1
Figure 9. Recognition results of scenario 2

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The detailed result is presented in Tables 1 and 2. From Table 1 it can be seen that the
best result is K-1, whose accuracy is 92.72%. In Table 2, the best result is K-5 with the accuracy
of 89.36%. Seen from the computational time needed during the recognition process, scenario 1
takes shorter time that scenario 2 does, that is 0.616125 second dan 1.307945 second
respectively as shown in Figure 10.
Table 1. Recognition Results of Scenario 1
The
value
of K
Amount of
License Plate
Recognized of
License Plate
Accuracy of
License
Plate
Number of
Characters
True
Character
False
Character
Accuracy
of
Character
1 50 30 60% 357 331 26 92.72%
3 50 30 60% 357 329 28 92.16%
5 50 30 60% 357 330 27 92.44%
Table 2. Recognition Results of Scenario 2
The
value
of K
Amount of
License Plate
Recognized of
License Plate
Accuracy of
License
Plate
Number of
Characters
True
Character
False
Character
Accuracy
Of
Character
1 50 10 20% 357 278 79 77.87%
3 50 20 40% 357 304 53 85.15%
5 50 26 52% 357 319 38 89.36%
Figure 10. The result of the process of recognizing the character of the vehicle license pelate
(a) features 10x10 area (b) features 20x20 area
Table 3 presents the comparison of the trial result using the method applied in this
research and the trial result using other method in the previous research. In the recognition of
the plate’s character the method we propose is better than the method of feature from
interconnected image applied in the research [6]. Despite the fact that the accuracy we gain in
this research cannot exceed the accuracy gained in the research [7] which applied DCT, we
applied the method to all types of vehicle license plate; while the research [7] applied its method
only to one type of license plate as shown in Figure 1.
Table 3. Comparison of Trial Results
Methods Accuracy
Feature from interconnected image segmentation [6] Character : 85%
Localization plate (Detection) : 97%
Discrete Cosine Transform [7] Character : 96%
Area Feature Extraction (Research conducted) Character : 92.72%
Detection : 99.44%
License Plate : 60%
4. Conclusion
From the research that has been done, the method of recognizing the character of vehicle
license plate is able to recognize the character very well which is proven with the result of

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accuracy reaching 92.72%. The feature and value areas of K, both have a role in recognizing the
character of the vehicle license plate so it is necessary to test the feature area and the value of K
to get the most accuracy. In this study, the computational time required by the system to
recognize the character of the average vehicle license plate is 0.62 seconds in the area of 10x10
and 1.31 seconds in the 20x20 area. The computational time difference is caused by the number
of feature values on each processed image, thus causing the 20x20 area to be longer than the
10x10 area.
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